Console

1. In the Google Cloud console, go to theVertex AI Studio> MediaStudio page.

   Media Studio

2. ClickVideo.

3. Optional: In theSettings pane, configure the following settings:

   • Model: choose a model from the available options.

   • Aspect ratio: choose either16:9 or9:16.

   • Number of results: adjust the slider or enter a value between1and4.

   • Video length: select the desired video length from the menu.

   • Output directory: clickBrowse to create or select aCloud Storage bucket to store output files.

4. Optional: In theSafety section, select one of the followingPersongeneration settings:

   • Allow (Adults only): default value. Generate adult people or facesonly. Don't generate youth or children people or faces.

   • Don't allow: don't generate people or faces.

5. Optional: In theAdvanced options section, enter aSeed value forrandomizing video generation.

6. In theWrite your prompt prompt box, clickuploadUpload.

7. Choose a local image to upload and clickSelect.

8. In theWrite your prompt box, enter your text prompt that describes thevideos to generate.

9. ClicksendGenerate.

Python

Install

pip install --upgrade google-genai

To learn more, see the SDK reference documentation.

Set environment variables to use the Gen AI SDK with Vertex AI:

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values# with appropriate values for your project.exportGOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECTexportGOOGLE_CLOUD_LOCATION=globalexportGOOGLE_GENAI_USE_VERTEXAI=True

importtimefromgoogleimportgenaifromgoogle.genai.typesimportGenerateVideosConfig,Imageclient=genai.Client()# TODO(developer): Update and un-comment below line# output_gcs_uri = "gs://your-bucket/your-prefix"operation=client.models.generate_videos(model="veo-3.1-generate-001",prompt="Extreme close-up of a cluster of vibrant wildflowers swaying gently in a sun-drenched meadow.",image=Image(gcs_uri="gs://cloud-samples-data/generative-ai/image/flowers.png",mime_type="image/png",),config=GenerateVideosConfig(aspect_ratio="16:9",output_gcs_uri=output_gcs_uri,),)whilenotoperation.done:time.sleep(15)operation=client.operations.get(operation)print(operation)ifoperation.response:print(operation.result.generated_videos[0].video.uri)# Example response:# gs://your-bucket/your-prefix

REST

After youset up your environment, you can use REST to test a text prompt. The following sample sends a request to the publisher model endpoint.

For more information about the Veo API, see theVeo on Vertex AIAPI.

1. Use the following command to send a video generation request. Thisrequest begins a long-running operation and stores output to aCloud Storage bucket you specify.

   Before using any of the request data, make the following replacements:

   • PROJECT_ID: A string representing your Google Cloud project ID.
   • MODEL_ID: A string respresenting the model ID to use. The following are accepted values:
     • Veo 2:veo-2.0-generate-001
     • Veo 3:veo-3.0-generate-001
     • Veo 3.1:veo-3.1-generate-001
     • Veo 3.1:veo-3.1-fast-generate-001
   • TEXT_PROMPT: The text prompt used to guide video generation.
   • INPUT_IMAGE: A base64-encoded string that represents the input image. For best quality, we recommend that the input image's resolution be 720p (1280 x 720 pixels) or higher, and have an aspect ratio of either 16:9 or 9:16. Images of other aspect ratios or sizes may be resized or centrally cropped when the image is uploaded.
   • MIME_TYPE: A string representing the MIME type of the input image. Only the images of the following MIME types are supported:
     • "image/jpeg"
     • "image/png"
   • OUTPUT_STORAGE_URI: Optional: A string representing the Cloud Storage bucket to store the output videos. If not provided, video bytes are returned in the response. For example:"gs://video-bucket/output/".
   • RESIZE_MODE: A string that represents the resize mode to use. The following are accepted values:
     • "crop": Crop the video to fit the new size.
     • "pad": Pad the video to fit the new size.
   • RESPONSE_COUNT: The number of video files to generate. The accepted range of values is1-4.
   • DURATION: An integer representing the length of the generated video files. The following are the accepted values for each model:
     • Veo 2 models:5-8. The default is8.
     • Veo 3 models:4,6, or8. The default is8.

   • Additional optional parameters

     Use the following optional variables depending on your use case. Add some or all of the following parameters in the"parameters": {} object.

     "parameters": {  "aspectRatio": "ASPECT_RATIO",  "negativePrompt": "NEGATIVE_PROMPT",  "personGeneration": "PERSON_SAFETY_SETTING",  // "resolution":RESOLUTION, // Veo 3 models only  "sampleCount":RESPONSE_COUNT,  "seed":SEED_NUMBER}

     • ASPECT_RATIO: Optional: A string value that describes the aspect ratio of the generated videos. You can use the following values:
       • "16:9" for landscape
       • "9:16" for portrait

       The default value is"16:9"

     • NEGATIVE_PROMPT: Optional: A string value that describes content that you want to prevent the model from generating.
     • PERSON_SAFETY_SETTING: Optional: A string value that controls the safety setting for generating people or face generation. You can use the following values:
       • "allow_adult": Only allow generation of adult people and faces.
       • "disallow": Doesn't generate people or faces.

       The default value is"allow_adult".

     • RESOLUTION: Optional: A string value that controls the resolution of the generated video. Supported byVeo 3 models only. You can use the following values:
       • "720p"
       • "1080p"

       The default value is"720p".

     • RESPONSE_COUNT: Optional. An integer value that describes the number of videos to generate. The accepted range of values is1-4.
     • SEED_NUMBER: Optional. An uint32 value that the model uses to generate deterministic videos. Specifying a seed number with your request without changing other parameters guides the model to produce the same videos. The accepted range of values is0-4294967295.

   HTTP method and URL:

   POST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID:predictLongRunning

   Request JSON body:

   {  "instances": [    {      "prompt": "TEXT_PROMPT",      "image": {        "bytesBase64Encoded": "INPUT_IMAGE",        "mimeType": "MIME_TYPE"      }    }  ],  "parameters": {    "storageUri": "OUTPUT_STORAGE_URI",    "sampleCount":RESPONSE_COUNT    "resizeMode": "RESIZE_MODE"  }}

   To send your request, choose one of these options:

   curl

   Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login , or by usingCloud Shell, which automatically logs you into thegcloud CLI . You can check the currently active account by runninggcloud auth list.

   Save the request body in a file namedrequest.json, and execute the following command:

   curl -X POST \
        -H "Authorization: Bearer $(gcloud auth print-access-token)" \
        -H "Content-Type: application/json; charset=utf-8" \
        -d @request.json \
        "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID:predictLongRunning"

   PowerShell

   Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login . You can check the currently active account by runninggcloud auth list.

   Save the request body in a file namedrequest.json, and execute the following command:

   $cred = gcloud auth print-access-token
   $headers = @{ "Authorization" = "Bearer $cred" }
   
   Invoke-WebRequest `
       -Method POST `
       -Headers $headers `
       -ContentType: "application/json; charset=utf-8" `
       -InFile request.json `
       -Uri "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID:predictLongRunning" | Select-Object -Expand Content

   This request returns a full operation name with a unique operation ID. Use thisfull operation name to poll that status of the video generation request.
   {  "name": "projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8"}

2. Optional: Check the status of the video generation long-runningoperation.

   Before using any of the request data, make the following replacements:

   • PROJECT_ID: Your Google Cloudproject ID.
   • MODEL_ID: The model ID to use.
   • OPERATION_ID: The unique operation ID returned in the original generate video request.

   HTTP method and URL:

   POST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID:fetchPredictOperation

   Request JSON body:

   {  "operationName": "projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID"}

   To send your request, choose one of these options:

   curl

   Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login , or by usingCloud Shell, which automatically logs you into thegcloud CLI . You can check the currently active account by runninggcloud auth list.

   Save the request body in a file namedrequest.json, and execute the following command:

   curl -X POST \
        -H "Authorization: Bearer $(gcloud auth print-access-token)" \
        -H "Content-Type: application/json; charset=utf-8" \
        -d @request.json \
        "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID:fetchPredictOperation"

   PowerShell

   Note: The following command assumes that you have logged in to thegcloud CLI with your user account by runninggcloud init orgcloud auth login . You can check the currently active account by runninggcloud auth list.

   Save the request body in a file namedrequest.json, and execute the following command:

   $cred = gcloud auth print-access-token
   $headers = @{ "Authorization" = "Bearer $cred" }
   
   Invoke-WebRequest `
       -Method POST `
       -Headers $headers `
       -ContentType: "application/json; charset=utf-8" `
       -InFile request.json `
       -Uri "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID:fetchPredictOperation" | Select-Object -Expand Content

   This request returns information about the operation, including if the operation is still runningor is done.

   Response

   {  "name": "projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID",  "done": true,  "response": {    "raiMediaFilteredCount": 0,    "@type": "type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse",    "videos": [      {        "gcsUri":"gs://BUCKET_NAME/TIMESTAMPED_FOLDER/sample_0.mp4",        "mimeType": "video/mp4"      }    ]  }}